OpenRaiser/Intern-Atlas huggingface dataset:
DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
Venue: arXiv cs.CV 2018
Year: 2018

We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment with a very limited amount of information or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.
OMG - Emotion Challenge Solution
Venue: arXiv cs.CV 2018
Year: 2018

This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then used a spectral approach to fuse them to obtain improved performance.
Deep Factorization Machines for Knowledge Tracing
Venue: arXiv cs.IR 2018
Year: 2018

This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Venue: arXiv cs.CV 2018
Year: 2018

Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and observer-dependent. Convolutional neural networks have made improvements in computer vision and in medical image analysis. They have successfully been applied to segmentation tasks and were extended to work on volumetric data. In this paper we introduce a combined deep-learning based approach on volumetric segmentation in Ultrasound acquisitions with incorporation of prior knowledge about left atrial shape and imaging device. The results show, that including a shape prior helps the domain adaptation and the accuracy of segmentation is further increased with adversarial learning.
Use of NoSQL database and visualization techniques to analyze massive scholarly article data from journals
Venue: arXiv cs.DL 2018
Year: 2018

Visualization of the massive data is a challenging endeavor. Extracting data and providing graphical representations can aid in its effective utilization in terms of interpretation and knowledge discovery. Publishing research articles has become a way of life for academicians. The scholarly publications can shape-up the professional growth of authors and also expand the research and technological growth of a country, continent and other demographic regions. Scholarly articles have grown in gigantic numbers that are published in different domains by various journals. Information related to articles, authors, their affiliations, number of citations, country, publisher, references and other information is like a gold mine for statisticians and data analysts. This data when used skillfully, via visual analysis tool, can provide valuable understanding and can aid in deeper exposition for researchers working in domains like scientometrics and bibliometrics. Since the data is not readily available, we used Google scholar, a comprehensive and free repository of scholarly articles, as data source for our study. Data was scraped from Google scholar and stored as a graph and later visualized in the form of nodes and its relationships, which offered discerning and concealed information of growing impact of articles, journals and authors in their domains. Not only this, evident domain shift of an author, various research domains spread for an author, predicting emerging domain and subdomains, detecting cartel behavior at Journal and author-level was also depicted by graphical analysis. Neo4j graph database was used in the background to help store the data in structured manner.
Updating Content in Cache-Aided Coded Multicast
Venue: arXiv cs.IT 2018
Year: 2018

Motivated by applications to delivery of dynamically updated, but correlated data in settings such as content distribution networks, and distributed file sharing systems, we study a single source multiple destination network coded multicast problem in a cache-aided network. We focus on models where the caches are primarily located near the destinations, and where the source has no cache. The source observes a sequence of correlated frames, and is expected to do frame-by-frame encoding with no access to prior frames. We present a novel scheme that shows how the caches can be advantageously used to decrease the overall cost of multicast, even though the source encodes without access to past data. Our cache design and update scheme works with any choice of network code designed for a corresponding cache-less network, is largely decentralized, and works for an arbitrary network. We study a convex relation of the optimization problem that results form the overall cost function. The results of the optimization problem determines the rate allocation and caching strategies. Numerous simulation results are presented to substantiate the theory developed.
Index-Stratified Types (Extended Version)
Venue: arXiv cs.PL 2018
Year: 2018

We present Tores, a core language for encoding metatheoretic proofs. The novel features we introduce are well-founded Mendler-style (co)recursion over indexed data types and a form of recursion over objects in the index language to build new types. The latter, which we call index-stratified types, are analogue to the concept of large elimination in dependently typed languages. These features combined allow us to encode sophisticated case studies such as normalization for lambda calculi and normalization by evaluation. We prove the soundness of Tores as a programming and proof language via the key theorems of subject reduction and termination.
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
Venue: arXiv cs.SI 2018
Year: 2018

Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive sub-websites from the StackExchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive sub-websites. These patterns are mainly: cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactory prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are: (1) the decay process is not governed by only one network measure; it is better described using multiple measures; (2) the expert members of the StackExchange sub-websites were mainly responsible for the activity or inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is going through decay dynamics that may lead to it becoming fully-decayed; and (4) decayed sub-websites were originally less resilient to inactivity decay, unlike the alive sub-websites.
A Cooperative Freeway Merge Assistance System using Connected Vehicles
Venue: arXiv cs.CY 2018
Year: 2018

The rapid growth of traffic-related fatalities and injuries around the world including developed countries has drawn researchers' attention for conducting research on automated highway systems to improve road safety over the past few years. In addition, fuel expenses due to traffic congestion in the U.S. translate to billions of dollars annually. These issues are motivating researchers across many disciplines to develop strategies to implement automation in transportation. The advent of connected-vehicle (CV) technology has added a new dimension to the research. The CV technology allows a vehicle to communicate with roadside infrastructure (vehicle-to-infrastructure), and other vehicles (vehicle-to-vehicle) on roads wirelessly using dedicated short-range communication (DSRC) protocol. Collectively, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies are known as V2X technology. Automotive companies have started to include On-Board Units (OBUs) on latest automobiles which can run safety-critical and assistive applications using V2X technology. For example, US Department of Transportation has already launched various applications including but not limited to lane-change assistance, collision avoidance, SPaT for emergency and transit vehicles. Merge conflicts, especially when vehicles are trying to merge from ramps to freeways, are a significant source of collisions, traffic congestion and fuel use. This paper describes a novel freeway merge assistance system utilizing V2X technology with the help of the DSRC protocol. The freeway merge assistance system uses an innovative three-way handshaking protocol and provides advisories to drivers to guide the merging sequence.
Exploring Conversational Language Generation for Rich Content about Hotels
Venue: arXiv cs.CL 2018
Year: 2018

Dialogue systems for hotel and tourist information have typically simplified the richness of the domain, focusing system utterances on only a few selected attributes such as price, location and type of rooms. However, much more content is typically available for hotels, often as many as 50 distinct instantiated attributes for an individual entity. New methods are needed to use this content to generate natural dialogues for hotel information, and in general for any domain with such rich complex content. We describe three experiments aimed at collecting data that can inform an NLG for hotels dialogues, and show, not surprisingly, that the sentences in the original written hotel descriptions provided on webpages for each hotel are stylistically not a very good match for conversational interaction. We quantify the stylistic features that characterize the differences between the original textual data and the collected dialogic data. We plan to use these in stylistic models for generation, and for scoring retrieved utterances for use in hotel dialogues
SAGE: Percipient Storage for Exascale Data Centric Computing
Venue: arXiv cs.DC 2018
Year: 2018

We aim to implement a Big Data/Extreme Computing (BDEC) capable system infrastructure as we head towards the era of Exascale computing - termed SAGE (Percipient StorAGe for Exascale Data Centric Computing). The SAGE system will be capable of storing and processing immense volumes of data at the Exascale regime, and provide the capability for Exascale class applications to use such a storage infrastructure. SAGE addresses the increasing overlaps between Big Data Analysis and HPC in an era of next-generation data centric computing that has developed due to the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors, whose data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. Indeed, Exascale I/O, as a problem that has not been sufficiently dealt with for simulation codes, is appropriately addressed by the SAGE platform. The objective of this paper is to discuss the software architecture of the SAGE system and look at early results we have obtained employing some of its key methodologies, as the system continues to evolve.
Privacy preserving distributed optimization using homomorphic encryption
Venue: arXiv cs.CR 2018
Year: 2018

This paper studies how a system operator and a set of agents securely execute a distributed projected gradient-based algorithm. In particular, each participant holds a set of problem coefficients and/or states whose values are private to the data owner. The concerned problem raises two questions: how to securely compute given functions; and which functions should be computed in the first place. For the first question, by using the techniques of homomorphic encryption, we propose novel algorithms which can achieve secure multiparty computation with perfect correctness. For the second question, we identify a class of functions which can be securely computed. The correctness and computational efficiency of the proposed algorithms are verified by two case studies of power systems, one on a demand response problem and the other on an optimal power flow problem.
Information Loss in the Human Auditory System
Venue: arXiv cs.IT 2018
Year: 2018

From the eardrum to the auditory cortex, where acoustic stimuli are decoded, there are several stages of auditory processing and transmission where information may potentially get lost. In this paper, we aim at quantifying the information loss in the human auditory system by using information theoretic tools. To do so, we consider a speech communication model, where words are uttered and sent through a noisy channel, and then received and processed by a human listener. We define a notion of information loss that is related to the human word recognition rate. To assess the word recognition rate of humans, we conduct a closed-vocabulary intelligibility test. We derive upper and lower bounds on the information loss. Simulations reveal that the bounds are tight and we observe that the information loss in the human auditory system increases as the signal to noise ratio (SNR) decreases. Our framework also allows us to study whether humans are optimal in terms of speech perception in a noisy environment. Towards that end, we derive optimal classifiers and compare the human and machine performance in terms of information loss and word recognition rate. We observe a higher information loss and lower word recognition rate for humans compared to the optimal classifiers. In fact, depending on the SNR, the machine classifier may outperform humans by as much as 8 dB. This implies that for the speech-in-stationary-noise setup considered here, the human auditory system is sub-optimal for recognizing noisy words.
Alternative passive maps in the Brayton-Moser framework: Implications on control and optimization
Venue: arXiv cs.SY 2018
Year: 2018

In the recent years, passivity theory has gained renewed attention because of its advantages and practicality in modeling of multi-domain systems and constructive control techniques. Unlike Lyapunov theory, passivity theory takes a behavioral approach in its control design methodologies. Hence, it provides solutions, which not only achieve the control objectives, but are also easily interpretable in the standard engineering parlance. The fundamental idea in passivity based control (PBC) methodologies is to find a controller that renders the closed-loop system passive. It is well known that, the PBC methodologies that rely on power-conjugate port-variables do not work for control objectives that require bounded power and unbounded energy. This is commonly known as the dissipation obstacle. One possible alternative that has been well explored, in the case of finite dimensional systems, is Brayton-Moser formulation. However, designing controllers in this framework leads to various difficulties, such as, solving for partial differential equations and finding storage functions satisfying a gradient structure.
One Theorem to Rule Them All: A Unified Translation of LTL into ω-Automata
Venue: arXiv cs.LO 2018
Year: 2018

We present a unified translation of LTL formulas into deterministic Rabin automata, limit-deterministic Büchi automata, and nondeterministic Büchi automata. The translations yield automata of asymptotically optimal size (double or single exponential, respectively). All three translations are derived from one single Master Theorem of purely logical nature. The Master Theorem decomposes the language of a formula into a positive boolean combination of languages that can be translated into ω-automata by elementary means. In particular, Safra's, ranking, and breakpoint constructions used in other translations are not needed.
Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range
Venue: arXiv cs.NA 2018
Year: 2018

Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range. In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15x faster, and requires 15x less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.
Breaking the $\tildeΩ(\sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
Venue: arXiv cs.DC 2018
Year: 2018

We study the consensus problem in a synchronous distributed system of $n$ nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and outgoing communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde Ω(\sqrt{n})$ rounds in an $n$-node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. Our main contributions are randomized distributed algorithms that achieve almost-everywhere consensus w.h.p. against a late adaptive adversary who can block up to $εn$ nodes in each round, for a small constant $ε>0$. Our first protocol achieves binary and plurality consensus, and the second one achieves multi-value consensus. Both of our algorithms succeed in $O(\log n)$ rounds with high probability, thus showing an exponential gap to the aforementioned lower bound for strongly adaptive crash-failure adversaries, which can be strengthened to $Ω(n)$ when allowing the adversary to block nodes instead of permanently crashing them. In our algorithms each node contacts only an (amortized) constant number of peers in each communication round. We show that our algorithms are optimal up to constant (resp. sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $Ω(\log_d n)$ rounds in the worst case, where $d$ is an upper bound on the number of communication requests initiated per node in each round. We complement our theoretical results with an experimental evaluation of the first protocol revealing a short convergence time.
Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era
Venue: arXiv cs.DB 2018
Year: 2018

In light of the emergence of big data, I have advocated and argued for a paradigm shift from Tobler's law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and - more importantly - from Descartes' mechanistic thinking to Alexander's organic thinking. Fractal geometry falls under the third definition of fractal - that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times (Jiang and Yin 2014) - rather than under the second definition of fractal, which requires a power law between scales and details (Mandelbrot 1982). The new fractal geometry is more towards living geometry that "follows the rules, constraints, and contingent conditions that are, inevitably, encountered in the real world" (Alexander et al. 2012, p. 395), not only for understanding complexity, but also for creating complex or living structure (Alexander 2002-2005). This editorial attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.
How does the AI understand what's going on
Venue: arXiv cs.AI 2018
Year: 2018

Most researchers regard AI as a static function without memory. This is one of the few articles where AI is seen as a device with memory. When we have memory, we can ask ourselves: "Where am I?", and "What is going on?" When we have no memory, we have to assume that we are always in the same place and that the world is always in the same state.
A new analysis of Work Stealing with latency
Venue: arXiv cs.DC 2018
Year: 2018

We study in this paper the impact of communication latency on the classical Work Stealing load balancing algorithm. Our paper extends the reference model in which we introduce a latency parameter. By using a theoretical analysis and simulation, we study the overall impact of this latency on the Makespan (maximum completion time). We derive a new expression of the expected running time of a bag of tasks scheduled by Work Stealing. This expression enables us to predict under which conditions a given run will yield acceptable performance. For instance, we can easily calibrate the maximal number of processors to use for a given work/platform combination. All our results are validated through simulation on a wide range of parameters.